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<li><a class="reference internal" href="#">Advanced Plotting With Partial Dependence</a><ul>
<li><a class="reference internal" href="#train-models-on-the-boston-housing-price-dataset">Train models on the boston housing price dataset</a></li>
<li><a class="reference internal" href="#plotting-partial-dependence-for-two-features">Plotting partial dependence for two features</a></li>
<li><a class="reference internal" href="#plotting-partial-dependence-of-the-two-models-together">Plotting partial dependence of the two models together</a></li>
<li><a class="reference internal" href="#plotting-partial-dependence-for-one-feature">Plotting partial dependence for one feature</a></li>
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  <div class="sphx-glr-download-link-note admonition note">
<p class="admonition-title">Note</p>
<p>Click <a class="reference internal" href="#sphx-glr-download-auto-examples-plot-partial-dependence-visualization-api-py"><span class="std std-ref">here</span></a> to download the full example code or to run this example in your browser via Binder</p>
</div>
<div class="sphx-glr-example-title section" id="advanced-plotting-with-partial-dependence">
<span id="sphx-glr-auto-examples-plot-partial-dependence-visualization-api-py"></span><h1>Advanced Plotting With Partial Dependence<a class="headerlink" href="#advanced-plotting-with-partial-dependence" title="Permalink to this headline">¶</a></h1>
<p>The <a class="reference internal" href="../modules/generated/sklearn.inspection.plot_partial_dependence.html#sklearn.inspection.plot_partial_dependence" title="sklearn.inspection.plot_partial_dependence"><code class="xref py py-func docutils literal notranslate"><span class="pre">plot_partial_dependence</span></code></a> function returns a
<a class="reference internal" href="../modules/generated/sklearn.inspection.PartialDependenceDisplay.html#sklearn.inspection.PartialDependenceDisplay" title="sklearn.inspection.PartialDependenceDisplay"><code class="xref py py-class docutils literal notranslate"><span class="pre">PartialDependenceDisplay</span></code></a> object that can be used
for plotting without needing to recalculate the partial dependence. In this
example, we show how to plot partial dependence plots and how to quickly
customize the plot with the visualization API.</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>See also <a class="reference internal" href="plot_roc_curve_visualization_api.html#sphx-glr-auto-examples-plot-roc-curve-visualization-api-py"><span class="std std-ref">ROC Curve with Visualization API</span></a></p>
</div>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="vm">__doc__</span><span class="p">)</span>

<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
<span class="kn">from</span> <span class="nn">sklearn.datasets</span> <span class="kn">import</span> <span class="n">load_boston</span>
<span class="kn">from</span> <span class="nn">sklearn.neural_network</span> <span class="kn">import</span> <span class="n">MLPRegressor</span>
<span class="kn">from</span> <span class="nn">sklearn.preprocessing</span> <span class="kn">import</span> <span class="n">StandardScaler</span>
<span class="kn">from</span> <span class="nn">sklearn.pipeline</span> <span class="kn">import</span> <span class="n">make_pipeline</span>
<span class="kn">from</span> <span class="nn">sklearn.tree</span> <span class="kn">import</span> <span class="n">DecisionTreeRegressor</span>
<span class="kn">from</span> <span class="nn">sklearn.inspection</span> <span class="kn">import</span> <span class="n">plot_partial_dependence</span>
</pre></div>
</div>
<div class="section" id="train-models-on-the-boston-housing-price-dataset">
<h2>Train models on the boston housing price dataset<a class="headerlink" href="#train-models-on-the-boston-housing-price-dataset" title="Permalink to this headline">¶</a></h2>
<p>First, we train a decision tree and a multi-layer perceptron on the boston
housing price dataset.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">boston</span> <span class="o">=</span> <span class="n">load_boston</span><span class="p">()</span>
<span class="n">X</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">boston</span><span class="o">.</span><span class="n">data</span><span class="p">,</span> <span class="n">columns</span><span class="o">=</span><span class="n">boston</span><span class="o">.</span><span class="n">feature_names</span><span class="p">)</span>
<span class="n">y</span> <span class="o">=</span> <span class="n">boston</span><span class="o">.</span><span class="n">target</span>

<span class="n">tree</span> <span class="o">=</span> <span class="n">DecisionTreeRegressor</span><span class="p">()</span>
<span class="n">mlp</span> <span class="o">=</span> <span class="n">make_pipeline</span><span class="p">(</span><span class="n">StandardScaler</span><span class="p">(),</span>
                    <span class="n">MLPRegressor</span><span class="p">(</span><span class="n">hidden_layer_sizes</span><span class="o">=</span><span class="p">(</span><span class="mi">100</span><span class="p">,</span> <span class="mi">100</span><span class="p">),</span>
                                 <span class="n">tol</span><span class="o">=</span><span class="mf">1e-2</span><span class="p">,</span> <span class="n">max_iter</span><span class="o">=</span><span class="mi">500</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">))</span>
<span class="n">tree</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
<span class="n">mlp</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
</pre></div>
</div>
</div>
<div class="section" id="plotting-partial-dependence-for-two-features">
<h2>Plotting partial dependence for two features<a class="headerlink" href="#plotting-partial-dependence-for-two-features" title="Permalink to this headline">¶</a></h2>
<p>We plot partial dependence curves for features “LSTAT” and “RM” for
the decision tree. With two features,
<a class="reference internal" href="../modules/generated/sklearn.inspection.plot_partial_dependence.html#sklearn.inspection.plot_partial_dependence" title="sklearn.inspection.plot_partial_dependence"><code class="xref py py-func docutils literal notranslate"><span class="pre">plot_partial_dependence</span></code></a> expects to plot two
curves. Here the plot function place a grid of two plots using the space
defined by <code class="docutils literal notranslate"><span class="pre">ax</span></code> .</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">12</span><span class="p">,</span> <span class="mi">6</span><span class="p">))</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">&quot;Decision Tree&quot;</span><span class="p">)</span>
<span class="n">tree_disp</span> <span class="o">=</span> <span class="n">plot_partial_dependence</span><span class="p">(</span><span class="n">tree</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="p">[</span><span class="s2">&quot;LSTAT&quot;</span><span class="p">,</span> <span class="s2">&quot;RM&quot;</span><span class="p">],</span> <span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">)</span>
</pre></div>
</div>
<p>The partial depdendence curves can be plotted for the multi-layer perceptron.
In this case, <code class="docutils literal notranslate"><span class="pre">line_kw</span></code> is passed to
<a class="reference internal" href="../modules/generated/sklearn.inspection.plot_partial_dependence.html#sklearn.inspection.plot_partial_dependence" title="sklearn.inspection.plot_partial_dependence"><code class="xref py py-func docutils literal notranslate"><span class="pre">plot_partial_dependence</span></code></a> to change the color of
the curve.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">12</span><span class="p">,</span> <span class="mi">6</span><span class="p">))</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">&quot;Multi-layer Perceptron&quot;</span><span class="p">)</span>
<span class="n">mlp_disp</span> <span class="o">=</span> <span class="n">plot_partial_dependence</span><span class="p">(</span><span class="n">mlp</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="p">[</span><span class="s2">&quot;LSTAT&quot;</span><span class="p">,</span> <span class="s2">&quot;RM&quot;</span><span class="p">],</span> <span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">,</span>
                                   <span class="n">line_kw</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;c&quot;</span><span class="p">:</span> <span class="s2">&quot;red&quot;</span><span class="p">})</span>
</pre></div>
</div>
</div>
<div class="section" id="plotting-partial-dependence-of-the-two-models-together">
<h2>Plotting partial dependence of the two models together<a class="headerlink" href="#plotting-partial-dependence-of-the-two-models-together" title="Permalink to this headline">¶</a></h2>
<p>The <code class="docutils literal notranslate"><span class="pre">tree_disp</span></code> and <code class="docutils literal notranslate"><span class="pre">mlp_disp</span></code>
<a class="reference internal" href="../modules/generated/sklearn.inspection.PartialDependenceDisplay.html#sklearn.inspection.PartialDependenceDisplay" title="sklearn.inspection.PartialDependenceDisplay"><code class="xref py py-class docutils literal notranslate"><span class="pre">PartialDependenceDisplay</span></code></a> objects contain all the
computed information needed to recreate the partial dependence curves. This
means we can easily create additional plots without needing to recompute the
curves.</p>
<p>One way to plot the curves is to place them in the same figure, with the
curves of each model on each row. First, we create a figure with two axes
within two rows and one column. The two axes are passed to the
<a class="reference internal" href="../modules/generated/sklearn.inspection.PartialDependenceDisplay.html#sklearn.inspection.PartialDependenceDisplay.plot" title="sklearn.inspection.PartialDependenceDisplay.plot"><code class="xref py py-func docutils literal notranslate"><span class="pre">plot</span></code></a> functions of
<code class="docutils literal notranslate"><span class="pre">tree_disp</span></code> and <code class="docutils literal notranslate"><span class="pre">mlp_disp</span></code>. The given axes will be used by the plotting
function to draw the partial dependence. The resulting plot places the
decision tree partial dependence curves in the first row of the
multi-layer perceptron in the second row.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">fig</span><span class="p">,</span> <span class="p">(</span><span class="n">ax1</span><span class="p">,</span> <span class="n">ax2</span><span class="p">)</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">))</span>
<span class="n">tree_disp</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">ax</span><span class="o">=</span><span class="n">ax1</span><span class="p">)</span>
<span class="n">ax1</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">&quot;Decision Tree&quot;</span><span class="p">)</span>
<span class="n">mlp_disp</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">ax</span><span class="o">=</span><span class="n">ax2</span><span class="p">,</span> <span class="n">line_kw</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;c&quot;</span><span class="p">:</span> <span class="s2">&quot;red&quot;</span><span class="p">})</span>
<span class="n">ax2</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">&quot;Multi-layer Perceptron&quot;</span><span class="p">)</span>
</pre></div>
</div>
<p>Another way to compare the curves is to plot them on top of each other. Here,
we create a figure with one row and two columns. The axes are passed into the
<a class="reference internal" href="../modules/generated/sklearn.inspection.PartialDependenceDisplay.html#sklearn.inspection.PartialDependenceDisplay.plot" title="sklearn.inspection.PartialDependenceDisplay.plot"><code class="xref py py-func docutils literal notranslate"><span class="pre">plot</span></code></a> function as a list,
which will plot the partial dependence curves of each model on the same axes.
The length of the axes list must be equal to the number of plots drawn.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># Sets this image as the thumbnail for sphinx gallery</span>
<span class="c1"># sphinx_gallery_thumbnail_number = 4</span>
<span class="n">fig</span><span class="p">,</span> <span class="p">(</span><span class="n">ax1</span><span class="p">,</span> <span class="n">ax2</span><span class="p">)</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">6</span><span class="p">))</span>
<span class="n">tree_disp</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">ax</span><span class="o">=</span><span class="p">[</span><span class="n">ax1</span><span class="p">,</span> <span class="n">ax2</span><span class="p">],</span> <span class="n">line_kw</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;label&quot;</span><span class="p">:</span> <span class="s2">&quot;Decision Tree&quot;</span><span class="p">})</span>
<span class="n">mlp_disp</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">ax</span><span class="o">=</span><span class="p">[</span><span class="n">ax1</span><span class="p">,</span> <span class="n">ax2</span><span class="p">],</span> <span class="n">line_kw</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;label&quot;</span><span class="p">:</span> <span class="s2">&quot;Multi-layer Perceptron&quot;</span><span class="p">,</span>
                                      <span class="s2">&quot;c&quot;</span><span class="p">:</span> <span class="s2">&quot;red&quot;</span><span class="p">})</span>
<span class="n">ax1</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
<span class="n">ax2</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
</pre></div>
</div>
<p><code class="docutils literal notranslate"><span class="pre">tree_disp.axes_</span></code> is a numpy array container the axes used to draw the
partial dependence plots. This can be passed to <code class="docutils literal notranslate"><span class="pre">mlp_disp</span></code> to have the same
affect of drawing the plots on top of each other. Furthermore, the
<code class="docutils literal notranslate"><span class="pre">mlp_disp.figure_</span></code> stores the figure, which allows for resizing the figure
after calling <code class="docutils literal notranslate"><span class="pre">plot</span></code>. In this case <code class="docutils literal notranslate"><span class="pre">tree_disp.axes_</span></code> has two dimensions, thus
<code class="docutils literal notranslate"><span class="pre">plot</span></code> will only show the y label and y ticks on the left most plot.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">tree_disp</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">line_kw</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;label&quot;</span><span class="p">:</span> <span class="s2">&quot;Decision Tree&quot;</span><span class="p">})</span>
<span class="n">mlp_disp</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">line_kw</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;label&quot;</span><span class="p">:</span> <span class="s2">&quot;Multi-layer Perceptron&quot;</span><span class="p">,</span> <span class="s2">&quot;c&quot;</span><span class="p">:</span> <span class="s2">&quot;red&quot;</span><span class="p">},</span>
              <span class="n">ax</span><span class="o">=</span><span class="n">tree_disp</span><span class="o">.</span><span class="n">axes_</span><span class="p">)</span>
<span class="n">tree_disp</span><span class="o">.</span><span class="n">figure_</span><span class="o">.</span><span class="n">set_size_inches</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">6</span><span class="p">)</span>
<span class="n">tree_disp</span><span class="o">.</span><span class="n">axes_</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
<span class="n">tree_disp</span><span class="o">.</span><span class="n">axes_</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
</pre></div>
</div>
</div>
<div class="section" id="plotting-partial-dependence-for-one-feature">
<h2>Plotting partial dependence for one feature<a class="headerlink" href="#plotting-partial-dependence-for-one-feature" title="Permalink to this headline">¶</a></h2>
<p>Here, we plot the partial dependence curves for a single feature, “LSTAT”, on
the same axes. In this case, <code class="docutils literal notranslate"><span class="pre">tree_disp.axes_</span></code> is passed into the second
plot function.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">tree_disp</span> <span class="o">=</span> <span class="n">plot_partial_dependence</span><span class="p">(</span><span class="n">tree</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="p">[</span><span class="s2">&quot;LSTAT&quot;</span><span class="p">])</span>
<span class="n">mlp_disp</span> <span class="o">=</span> <span class="n">plot_partial_dependence</span><span class="p">(</span><span class="n">mlp</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="p">[</span><span class="s2">&quot;LSTAT&quot;</span><span class="p">],</span>
                                   <span class="n">ax</span><span class="o">=</span><span class="n">tree_disp</span><span class="o">.</span><span class="n">axes_</span><span class="p">,</span> <span class="n">line_kw</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;c&quot;</span><span class="p">:</span> <span class="s2">&quot;red&quot;</span><span class="p">})</span>
</pre></div>
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